Advances in Engineering: an International Journal (ADEIJ)
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Important Date: September 04, 2021
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Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html ;
Paper Submission Authors are invited to submit papers for this journal.
Important Date: September 04, 2021
Contact Us: adeij.journal@airccse.com
HYDROGEN DIFFUSION IN INDIUM OXIDE
Hui Xu
ABSTRACT
Hydrogen is an n-type dopant in In2O3, a conducting oxide. Hydrogen is an important source of n-type conductivity in In2O3 and has been shown by studies to be a shallow donor.1 Using data from an IR absorption experiment2 that studied a vibrational line at 3306cm-1 using Fourier Transform InfraRed spectrometry, hydrogen’s diffusivity is examined in this work. Hydrogen’s diffusivity is studied through its in-diffusion and out-diffusion in In2O3 single crystals3 . The diffusion process has been simulated in Python language by solving Fick’s second law with the appropriate boundary conditions. Hydrogen in-diffusion data was fitted using Scipy4 curve fit function. Fick’s second law, a second-order partial differential equation, has been solved using Fipy5 , a partial differential equation (PDE) solver written in Python. The simulation result was then fitted to published experimental data3 through regression and error analysis to determine the diffusivity of Hi+ in In2O3.
KEYWORDS
Diffusion, hydrogen, temperature, oxide
Full Text: https://airccse.com/adeij/papers/3120adeij02.pdf
Volume Link: https://airccse.com/adeij/vol3.html
Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html ;
Paper Submission Authors are invited to submit papers for this journal.
Important Date: August 21, 2021
Contact Us: adeij.journal@airccse.com
Henry Gao
Student, Bur Oak Secondary School
ABSTRACT
Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner).
KEYWORDS
Deep Learning, Natural Language Processing, BERT, distilBERT
Full Text: https://airccse.com/adeij/papers/3221adeij06.pdf
Volume Link: https://airccse.com/adeij/vol3.html
Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html
Paper Submission
Authors are invited to submit papers for this journal.
Important Date: August 21, 2021
Contact Us: adeij.journal@airccse.com
Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html
Paper Submission
Authors are invited to submit papers for this journal.
Important Date: August 21, 2021
Contact Us: adeij.journal@airccse.com
Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html
Paper Submission
Authors are invited to submit papers for this journal.
Important Date: August 21, 2021
Contact Us: adeij.journal@airccse.com
Advances in Engineering: an International Journal (ADEIJ)
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Paper Submission
Authors are invited to submit papers for this journal.
Important Date: August 07, 2021
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#machine learning #CS & IT Proceedings
2nd International Conference on Machine Learning &Trends (MLT 2021)
July 24 ~ 25, 2021, London, United Kingdom
Volume 11, Number 11, July 2021
Volume Editors : David C. Wyld, Dhinaharan Nagamalai (Eds)
Proceedings URL : https://airccse.org/csit/V11N11.html
Presentation URL : https://youtube.com/playlist?list=PLkPaq00oPRfxDAJA4pCFc9bH4IB-m7Xl2
Volume 11 : Number 11 : Computer Science & Information Technology (CS & IT)
https://airccse.org
Volume 11, Number 11, July 2021 - YouTube
https://youtube.com
Henry Gao Student, Bur Oak Secondary School
ABSTRACT
Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner).
KEYWORDS
Deep Learning, Natural Language Processing, BERT, distilBERT
Full Text: https://airccse.com/adeij/papers/3221adeij06.pdf
Volume Link: https://airccse.com/adeij/vol3.html
#ai #medicaleducation #llm #naturallanguageprocessing #linguistics The User-Centered Iterative Design of an LLM-Powered Educational Scenario...
